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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks
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Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks

机译：密度编码支持资源有效的随机连接的神经网络

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摘要

The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this brief, we focus on resource-efficient randomly connected neural networks known as random vector functional link (RVFL) networks since their simple design and extremely fast training time make them very attractive for solving many applied classification tasks. We propose to represent input features via the density-based encoding known in the area of stochastic computing and use the operations of binding and bundling from the area of hyperdimensional computing for obtaining the activations of the hidden neurons. Using a collection of 121 real-world data sets from the UCI machine learning repository, we empirically show that the proposed approach demonstrates higher average accuracy than the conventional RVFL. We also demonstrate that it is possible to represent the readout matrix using only integers in a limited range with minimal loss in the accuracy. In this case, the proposed approach operates only on small n-bits integers, which results in a computationally efficient architecture. Finally, through hardware field-programmable gate array (FPGA) implementations, we show that such an approach consumes approximately 11 times less energy than that of the conventional RVFL.
机译：资源受限边缘设备的机器学习算法部署是理论和应用点的重要挑战。在此简介中，我们专注于资源有效的随机连接的神经网络，称为随机矢量功能链路（RVFL）网络，因为它们简单的设计和极快的训练时间使它们非常有吸引力，可以解决许多应用的分类任务。我们建议通过随机计算区域中已知的基于密度的编码来表示输入特征，并使用从超级计算区域的绑定和捆绑的操作来获得隐藏神经元的激活。使用来自UCI机器学习存储库的121个现实数据集的集合，我们经验证明所提出的方法比传统的RVFL表现出更高的平均精度。我们还证明，可以仅在有限范围内使用整数来表示读出矩阵，其精度最小损耗。在这种情况下，所提出的方法仅在小型n位整数上运行，这导致计算有效的架构。最后，通过硬件现场可编程门阵列（FPGA）实现，我们表明这种方法消耗比传统RVFL的能量少大约11倍。

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